Claude Opus 5.5 vs GPT-6 Astra: Which Fits Long-Running Agent Work?
Compare task economics, context, computer use, coding, safeguards, and review burden without treating one benchmark as a verdict.
Claude Opus 5.5 vs GPT-6 Astra: Which Fits Long-Running Agent Work?
Claude Opus 5.5 and GPT-6 Astra are both built for long-running Agent work. Their providers emphasize overlapping strengths—coding, professional knowledge work, computer use, and stronger boundary behavior—but the practical choice depends on the workload, tool harness, economics, and where the model is actually available.
A benchmark table cannot make that decision by itself. Anthropic and OpenAI use different harnesses, effort settings, safeguards, and cost estimates. Each provider also reports some competitor results from the other provider. Treat those numbers as evidence to design a test, not as a universal ranking.
The short decision
Choose Claude Opus 5.5 when token efficiency, long coding sessions, concise communication, and Claude-native tool loops matter most, and your platform supports the model and its migration requirements.
Choose GPT-6 Astra when computer use, broad professional workflows, and OpenAI’s tool ecosystem matter most.
Use both when routing is based on measured task classes and the handoff state lives outside either model’s private context.
Specifications create a baseline, not a winner
Opus 5.5 has a 1M context window, 128K maximum output, adaptive thinking that is always on, and medium default effort. Anthropic lists $4 per million input tokens, $20 per million output tokens, and $0.20 per million cache reads.
GPT-6 Astra has a 1.05M context window, 128K maximum output, and effort levels from low through max. OpenAI lists $10 per million input tokens, $50 per million output tokens, and $1 per million cached input tokens. Inputs beyond 272K are priced at higher rates for the full request. These are the providers’ published API prices.
The small context-window difference is rarely the deciding factor. Retrieval quality, cache design, task decomposition, and persistent state usually matter more than fitting another 50,000 tokens.
Coding: compare the harness and acceptance tests
Anthropic reports Opus 5.5 at 66.4% on Terminal-Bench 4.0 at xhigh effort, while citing OpenAI’s 57.9% Astra result at high effort. On FrontierCode Main, Anthropic reports 54.4% for Opus 5.5 and cites 53.3% for Astra. OpenAI’s launch page reports Astra at 57.9% on Terminal-Bench and 53.3% on FrontierCode Main under its own setup.
Those numbers are close enough that repository fit can reverse the result. Test both on the same codebase with identical permissions, tools, time limits, tests, and reviewer rubric. Count accepted patches, failed loops, elapsed time, and total task cost.
Computer use and professional work
OpenAI positions Astra as its strongest computer-use model and reports 72.6% on its OSWorld 2.0 offline set, plus 41.4% on AutomationBench. Anthropic reports Opus 5.5 at 81.8% partial on its OSWorld setup and 40.0% on AutomationBench from Zapier. The OSWorld figures are not directly interchangeable because task and grading setups differ.
Astra’s product story is broad computer action and polished business artifacts. Opus 5.5’s story emphasizes long coding, knowledge work, fewer steps, and lower task cost relative to Opus 5. If the job crosses browser, spreadsheet, presentation, and desktop software, test the end-to-end artifact. If it is a multi-hour repository migration, test sustained engineering behavior.
Safety: neither provider claims controls are unnecessary
Anthropic reports stronger prompt-injection resistance and fewer containment-boundary attempts for Opus 5.5. OpenAI reports Astra stays within authorized scope more often than GPT-5.6 Sol, while also disclosing weaker chain-of-thought monitorability in adversarial tests. Both providers pair model behavior with safeguards and monitoring.
The operational rule is the same: restrict permissions, keep tool actions observable, require approval for consequential changes, and test hostile inputs in the real harness.
Workflow fit matters more than a catalog comparison
Buda keeps both models inside the same kind of observable work: files, tools, browser and terminal actions, artifacts, and review. The useful comparison is therefore not which provider has the longer feature list, but which model completes the chosen task with fewer failed loops and less reviewer repair.
A fair two-model evaluation
Select four tasks: repository coding, evidence-heavy research, cross-application execution, and a high-impact workflow requiring review. Freeze the inputs and tools. Run each model at a sensible default and one higher effort setting. Score completion, correctness, time, cost, interventions, and reviewer effort. Keep the artifacts and diffs.
Choose one default only after the results show a stable pattern. Route exceptions explicitly. A model portfolio is useful when it reduces failure and cost; it is not useful when every task starts with a leaderboard debate.
A routing rule needs a negative result too
Test one repository migration, one evidence-heavy report and one browser-to-document handoff on each model with the same materials and approval limits. Keep failures, tool interruptions and reviewer fixes in the dataset. If a coding patch passes only after a human rewrites the crucial part, count it as an assisted completion rather than a clean model win. If a computer-use task reaches the right screen but writes the wrong record, it fails the end-to-end task.
Choose a default only for a task class where repeated accepted outcomes support it. Record the exception rule, the handoff file or record, and the fallback when a tool fails. The decision can change as prompts, provider versions or the execution harness change; preserve the artifacts so the next comparison tests the same business result rather than a new benchmark setup.
Using Opus 5.5 in Buda
Claude Opus 5.5 is now selectable in Buda for Spaces with an active paid subscription, with a displayed multiplier of 1.3× relative to Sonnet 5 at 1.0×. Use the current pricing page for the other model’s access and multiplier. Compare Credits consumed per accepted result on the same task; neither API catalog prices nor displayed multipliers establish a fixed task-cost ratio.
Read the Buda launch announcement · Buda Credits